Remove Data Transformation Remove Structured Data Remove Testing
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Ensuring Data Transformation Quality with dbt Core

Wayne Yaddow

How dbt Core aids data teams test, validate, and monitor complex data transformations and conversions Photo by NASA on Unsplash Introduction dbt Core, an open-source framework for developing, testing, and documenting SQL-based data transformations, has become a must-have tool for modern data teams as the complexity of data pipelines grows.

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Data Engineers Are Using AI to Verify Data Transformations

Wayne Yaddow

AI is transforming how senior data engineers and data scientists validate data transformations and conversions. Artificial intelligence-based verification approaches aid in the detection of anomalies, the enforcement of data integrity, and the optimization of pipelines for improved efficiency.

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Apply fine-grained access and transformation on the SUPER data type in Amazon Redshift

AWS Big Data

Amazon Redshift, a cloud data warehouse service, supports attaching dynamic data masking (DDM) policies to paths of SUPER data type columns, and uses the OBJECT_TRANSFORM function with the SUPER data type. SUPER data type columns in Amazon Redshift contain semi-structured data like JSON documents.

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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

A modern data platform entails maintaining data across multiple layers, targeting diverse platform capabilities like high performance, ease of development, cost-effectiveness, and DataOps features such as CI/CD, lineage, and unit testing. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.

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Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

Amazon Athena provides interactive analytics service for analyzing the data in Amazon Simple Storage Service (Amazon S3). Amazon Redshift is used to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes.

Metadata 105
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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

For the downstream consumption by all departments across the organization, smava’s Data Platform team prepares curated data products following the extract, load, and transform (ELT) pattern. The data products from the Business Vault and Data Mart stages are now available for consumers.

Data Lake 115
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Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS

AWS Big Data

Spark SQL is an Apache Spark module for structured data processing. They use various AWS analytics services, such as Amazon EMR, to enable their analysts and data scientists to apply advanced analytics techniques to interactively develop and test new surveillance patterns and improve investor protection.

Big Data 105